Accessing the Slicer through VBA

There may be times when we want to programmatically control slicers through VBA. For example, we may want to default a date to the current date or set a cost centre depending on who has opened the book. This post looks how a slicer can be controlled through VBA.

In this example, we have added a slicer to a worksheet that uses a date hierarchy as its source. Because, we have included all levels of the hierarchy when the slicer was setup, we get three individual slicers for each level of the hierarchy.

If we look at the settings for the slicer (right click on the slicer and select slicer settings), we can see that the slicer has a name and each level of the slicer hierarchy maintains the hierarchy level name. For example, the Slicer_Dates_Hie below has a level Year, Month and Day. Although we can change the name (for example the name Year in the picture below), the slicer retains the mdx level that the slicer belongs to.

Accessing the Slicer

We can access the slicer through the SlicerCaches object. This is as simple as declaring the slicer cache and referencing it to the name of the slicer we want to use. For example;

Dim sC As SlicerCache
Set sC = ActiveWorkbook.SlicerCaches(“Slicer_Dates_Hie”)

Navigating the Structure of the Slicer

Once we have a reference to the slicer we can navigate its structure using SlicerCacheLevels. For example we can determine the number of levels of the slicer and iterate over them with the following code.

Dim sC As SlicerCache
Dim sL As SlicerCacheLevel
Set sC = ActiveWorkbook.SlicerCaches(“Slicer_Dates_Hie”)

For Each sL In sC.SlicerCacheLevels
Debug.Print “Level ” + CStr(sL.Ordinal) + ” –> ” + sL.Name
Next sL

Naturally, the level can be accessed through the cache level ordinal to produce the same result. The highest level (year) takes the value 1 which increments for each level from the first level. There is always a level (ie level 1) even if the slicer is based on a single attribute.

Set sC = ActiveWorkbook.SlicerCaches(“Slicer_Dates_Hie”)
For i = 1 To sC.SlicerCacheLevels.Count
Debug.Print “Level ” + CStr(i) + ” –> ” + sC.SlicerCacheLevels(i).Name
Next i

Slicer Data Members

We can gain access to the data items through slicer items, as mdx attributes, they have a caption, value and a key (member unique name). For example the year 2011 in this slicer has a value of 2011 and a name (MDX unique name) of [Dates].[Dates Hie].[Year].&[2011]

Dim sC As SlicerCache
Dim SL As SlicerCacheLevel
Dim sI As SlicerItem

Set sC = ActiveWorkbook.SlicerCaches(“Slicer_Dates_Hie”)
Set SL = sC.SlicerCacheLevels(1)
Debug.Print “——————————————————————————”

For Each sI In SL.SlicerItems
Debug.Print “Caption –> ” & sI.Caption
Debug.Print “Value –> ” + CStr(sI.Value)
Debug.Print “Unique Name –> ” + sI.Name
Debug.Print “——————————————————————————”


Setting the Slicer Value

Slicer item selection must be set through the visible slicer items list and is specified using an array. For example, we could set the SlicerCache (selected items) to 2011 and 2012 with the following code;

sC.VisibleSlicerItemsList = Array(“[Dates].[Dates Hie].[Year].&[2011]”, “[Dates].[Dates Hie].[Year].&[2012]”)

The name selected must be a data member of the level. If not a runtime error will occur (as below)

Once the values are set, connected pivots are updated immediately

Member Iteration

Members can be easily iterated using the following code;

Dim sC As SlicerCache 
Dim SL As SlicerCacheLevel 
Dim sI As SlicerItem
Set sC = ActiveWorkbook.SlicerCaches("Slicer_Dates_Hie") 
Set SL = sC.SlicerCacheLevels(2)
For Each sI In SL.SlicerItems    
 sC.VisibleSlicerItemsList = Array(sI.Name) 


The control of slicers through VBA could be used to provide some very nice personalisation to work books.

NB:  If you liked this post you might be interested in this one.  In it, I discuss setting slicers through cell association to pivot rows.


Australian SQL Saturday Goes Live

The SQL Saturday tour of Australia and New Zealand has now gone live on the SQL Saturday website. You can check out each cities part of the tour out at the following sites;

Tabular verse Dimensional Design Concepts

The multidimensional model appears to provide a richer environment for model design. However, for the case of snapshot fact tables, the tabular design may offer a much faster design and refreshing options. This post looks at the snapshot design and how business requirements are modelled in both multidimensional and tabular platforms.

The Snapshot Fact

Snapshot data (or accumulating snapshot fact tables) are usually used to record process driven data where records change over time. Consider, for example, the tables FactResellerSales and FactInternetSales tables in AdventureWorks. Here, the record shows Order Date, Due Date and a Ship Date. When the sale is made, the Order Date is known and the Due date is probably known, however, the ship date can change and is only truly known after the order has shipped. For this type of fact table, data should be added when the sale occurs and updated as information about the sale comes to light (that is, when it is shipped).

Classic Star Modelling

The standard way to model this relationship is through the reuse of the date table. That is, each fact field relates to the same Date.DateKey field in the dates table. This is shown for the tabular and multidimensional design models below




Multidimensional Interpretation

When a cube is built from this design, the Dates dimension becomes a role playing dimension and is joined to the fact for each relationship identified in the data source view. Thus, the Dates dimension is reused by the cube and with the dimension names appearing as the name of the fact field (after camel case conversion). Notice that there is a single dimension in the solution however, there appears to be three date dimensions (Order Date, Due Date and Ship Date).

Solution View

Cube View


While this approach may provide answers to simple business questions (eg, What is that value / quantity of product shipped on date xyz), the modelling technique fails when the query becomes complicated across restrictive dates. For example, it is not straight forward to determine the quantity ordered, shipped and due on date xyz.

Tabular Interpretation

In contrast to the multidimensional model, the tabular model employees an active relationship as the default join between fact and dimensions and each table appears only once in the ‘dimensional representation’ of the model. For example, there is only one date table in the pivot view.

By default, aggregation functions will use this relationship. In this situation, the active relationship (solid line) is between the OrderDate and the Date table. A sum measure ( sum([OrderQuantity])
) defined without context will therefore show the quantity ordered on each date.

The tabular model also allows functions to specify which relationship will be used as joins between tables. Therefore, the quantity of products shipped on a date can be determined by specifying the relationship between ResellerSales.ShipDateKey and Dates.DateKey. For example,

Ship Quantity:=CALCULATE(sum([OrderQuantity]),USERELATIONSHIP(‘ResellerSales'[ShipDateKey],’Dates'[DateKey]))

This allows the determination of measures that relate to more generic dimensions. For example, we can easily define [Order Quantity], [Ship Quantity], [Due Quantity] which specifies these values by date. This is in direct contrast to the default multidimensional behaviour and allows for more native browsing. For example, the date x value pivot below quickly identifies the sparse nature and trend of adventure works data.

I had never looked at adventure works data like this before. Here we can easily see that products are ordered on the 1st of the month, shipped on the 8th and due on the 13th. There are very few exceptions to this in the fact data.



The UDM can be designed to produce this outcome; however, it is not part of the ‘default’ behaviour. One way to achieve this would be to conform all dates into a single field (for example through a union) and specify separate measures for each union join (ie; add the Order Data, then the Ship Data and finally the Due Date data). However, this would require longer load times (since we are effectively staking facts) and increase the measure group size. The tabular approach (in my opinion) is a much nicer compromise.

NB It is also easy to mimic the multidimensional behaviour in tabular. Mulitple date tables are added to the model (one for each fact date) and labelled ‘Ship Date’ , ‘Due Date’, …



Tentative Dates for SQL Saturday

Following from my previous post about the upcoming SQL Saturday events around Australia, the tentative dates for the events across Australia and New Zealand will be;

Brisbane (AU) – April 12
Wellington (NZ) – April 14
Canberra (AU) – April 19
Sydney (AU) – April 21
Adelaide (AU) – April 24
Perth (AU) – April 27 or 28

These dates are fairly fixed and, any changes will only be by 1 or 2 days at most.